Overview

Dataset statistics

Number of variables26
Number of observations180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.0 KiB
Average record size in memory216.0 B

Variable types

Numeric16
Categorical10

Alerts

fuel-type is highly imbalanced (51.4%)Imbalance
engine-location is highly imbalanced (87.8%)Imbalance
num-of-cylinders is highly imbalanced (58.1%)Imbalance
symboling has 61 (33.9%) zerosZeros

Reproduction

Analysis started2024-05-28 09:29:36.547000
Analysis finished2024-05-28 09:30:25.267893
Duration48.72 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75555556
Minimum-2
Maximum3
Zeros61
Zeros (%)33.9%
Negative21
Negative (%)11.7%
Memory size2.8 KiB
2024-05-28T15:00:25.438777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q31
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1752676
Coefficient of variation (CV)1.5555012
Kurtosis-0.37676035
Mean0.75555556
Median Absolute Deviation (MAD)1
Skewness0.23644264
Sum136
Variance1.3812539
MonotonicityNot monotonic
2024-05-28T15:00:25.656784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 61
33.9%
1 54
30.0%
2 26
14.4%
3 18
 
10.0%
-1 18
 
10.0%
-2 3
 
1.7%
ValueCountFrequency (%)
-2 3
 
1.7%
-1 18
 
10.0%
0 61
33.9%
1 54
30.0%
2 26
14.4%
3 18
 
10.0%
ValueCountFrequency (%)
3 18
 
10.0%
2 26
14.4%
1 54
30.0%
0 61
33.9%
-1 18
 
10.0%
-2 3
 
1.7%

normalized-losses
Real number (ℝ)

Distinct46
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.11111
Minimum65
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:25.883796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile76.85
Q1101
median115
Q3139
95-th percentile168
Maximum231
Range166
Interquartile range (IQR)38

Descriptive statistics

Standard deviation30.406536
Coefficient of variation (CV)0.2531534
Kurtosis0.39229198
Mean120.11111
Median Absolute Deviation (MAD)19
Skewness0.70623205
Sum21620
Variance924.55742
MonotonicityNot monotonic
2024-05-28T15:00:26.214744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
115 34
 
18.9%
161 11
 
6.1%
91 8
 
4.4%
150 7
 
3.9%
104 6
 
3.3%
128 6
 
3.3%
94 5
 
2.8%
65 5
 
2.8%
134 5
 
2.8%
102 5
 
2.8%
Other values (36) 88
48.9%
ValueCountFrequency (%)
65 5
2.8%
74 4
2.2%
77 1
 
0.6%
78 1
 
0.6%
81 2
 
1.1%
85 4
2.2%
87 2
 
1.1%
89 2
 
1.1%
91 8
4.4%
93 4
2.2%
ValueCountFrequency (%)
231 1
 
0.6%
194 2
 
1.1%
192 2
 
1.1%
188 2
 
1.1%
186 1
 
0.6%
168 5
2.8%
164 2
 
1.1%
161 11
6.1%
158 2
 
1.1%
154 3
 
1.7%

make
Categorical

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
toyota
27 
nissan
18 
mazda
17 
mitsubishi
11 
peugot
11 
Other values (17)
96 

Length

Max length13
Median length11
Mean length6.3777778
Min length3

Characters and Unicode

Total characters1148
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 27
15.0%
nissan 18
 
10.0%
mazda 17
 
9.4%
mitsubishi 11
 
6.1%
peugot 11
 
6.1%
volvo 11
 
6.1%
honda 10
 
5.6%
subaru 9
 
5.0%
dodge 7
 
3.9%
volkswagen 7
 
3.9%
Other values (12) 52
28.9%

Length

2024-05-28T15:00:26.541030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 27
15.0%
nissan 18
 
10.0%
mazda 17
 
9.4%
mitsubishi 11
 
6.1%
peugot 11
 
6.1%
volvo 11
 
6.1%
honda 10
 
5.6%
subaru 9
 
5.0%
bmw 7
 
3.9%
mercedes-benz 7
 
3.9%
Other values (12) 52
28.9%

Most occurring characters

ValueCountFrequency (%)
a 138
12.0%
o 130
 
11.3%
s 95
 
8.3%
t 86
 
7.5%
e 70
 
6.1%
u 64
 
5.6%
n 62
 
5.4%
i 61
 
5.3%
d 55
 
4.8%
m 51
 
4.4%
Other values (15) 336
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 138
12.0%
o 130
 
11.3%
s 95
 
8.3%
t 86
 
7.5%
e 70
 
6.1%
u 64
 
5.6%
n 62
 
5.4%
i 61
 
5.3%
d 55
 
4.8%
m 51
 
4.4%
Other values (15) 336
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 138
12.0%
o 130
 
11.3%
s 95
 
8.3%
t 86
 
7.5%
e 70
 
6.1%
u 64
 
5.6%
n 62
 
5.4%
i 61
 
5.3%
d 55
 
4.8%
m 51
 
4.4%
Other values (15) 336
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 138
12.0%
o 130
 
11.3%
s 95
 
8.3%
t 86
 
7.5%
e 70
 
6.1%
u 64
 
5.6%
n 62
 
5.4%
i 61
 
5.3%
d 55
 
4.8%
m 51
 
4.4%
Other values (15) 336
29.3%

fuel-type
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
gas
161 
diesel
19 

Length

Max length6
Median length3
Mean length3.3166667
Min length3

Characters and Unicode

Total characters597
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 161
89.4%
diesel 19
 
10.6%

Length

2024-05-28T15:00:26.836238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:27.065321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
gas 161
89.4%
diesel 19
 
10.6%

Most occurring characters

ValueCountFrequency (%)
s 180
30.2%
g 161
27.0%
a 161
27.0%
e 38
 
6.4%
d 19
 
3.2%
i 19
 
3.2%
l 19
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 180
30.2%
g 161
27.0%
a 161
27.0%
e 38
 
6.4%
d 19
 
3.2%
i 19
 
3.2%
l 19
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 180
30.2%
g 161
27.0%
a 161
27.0%
e 38
 
6.4%
d 19
 
3.2%
i 19
 
3.2%
l 19
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 597
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 180
30.2%
g 161
27.0%
a 161
27.0%
e 38
 
6.4%
d 19
 
3.2%
i 19
 
3.2%
l 19
 
3.2%

aspiration
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
std
148 
turbo
32 

Length

Max length5
Median length3
Mean length3.3555556
Min length3

Characters and Unicode

Total characters604
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 148
82.2%
turbo 32
 
17.8%

Length

2024-05-28T15:00:27.339849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:27.585553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
std 148
82.2%
turbo 32
 
17.8%

Most occurring characters

ValueCountFrequency (%)
t 180
29.8%
s 148
24.5%
d 148
24.5%
u 32
 
5.3%
r 32
 
5.3%
b 32
 
5.3%
o 32
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 180
29.8%
s 148
24.5%
d 148
24.5%
u 32
 
5.3%
r 32
 
5.3%
b 32
 
5.3%
o 32
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 180
29.8%
s 148
24.5%
d 148
24.5%
u 32
 
5.3%
r 32
 
5.3%
b 32
 
5.3%
o 32
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 180
29.8%
s 148
24.5%
d 148
24.5%
u 32
 
5.3%
r 32
 
5.3%
b 32
 
5.3%
o 32
 
5.3%

num-of-doors
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
four
106 
two
74 

Length

Max length4
Median length4
Mean length3.5888889
Min length3

Characters and Unicode

Total characters646
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 106
58.9%
two 74
41.1%

Length

2024-05-28T15:00:27.802962image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:28.027770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 106
58.9%
two 74
41.1%

Most occurring characters

ValueCountFrequency (%)
o 180
27.9%
f 106
16.4%
u 106
16.4%
r 106
16.4%
t 74
11.5%
w 74
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 180
27.9%
f 106
16.4%
u 106
16.4%
r 106
16.4%
t 74
11.5%
w 74
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 180
27.9%
f 106
16.4%
u 106
16.4%
r 106
16.4%
t 74
11.5%
w 74
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 646
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 180
27.9%
f 106
16.4%
u 106
16.4%
r 106
16.4%
t 74
11.5%
w 74
11.5%

body-style
Categorical

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
sedan
90 
hatchback
58 
wagon
21 
hardtop
 
8
convertible
 
3

Length

Max length11
Median length5
Mean length6.4777778
Min length5

Characters and Unicode

Total characters1166
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 90
50.0%
hatchback 58
32.2%
wagon 21
 
11.7%
hardtop 8
 
4.4%
convertible 3
 
1.7%

Length

2024-05-28T15:00:28.274587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:28.517457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 90
50.0%
hatchback 58
32.2%
wagon 21
 
11.7%
hardtop 8
 
4.4%
convertible 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 235
20.2%
h 124
10.6%
c 119
10.2%
n 114
9.8%
d 98
8.4%
e 96
8.2%
s 90
 
7.7%
t 69
 
5.9%
b 61
 
5.2%
k 58
 
5.0%
Other values (8) 102
8.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 235
20.2%
h 124
10.6%
c 119
10.2%
n 114
9.8%
d 98
8.4%
e 96
8.2%
s 90
 
7.7%
t 69
 
5.9%
b 61
 
5.2%
k 58
 
5.0%
Other values (8) 102
8.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 235
20.2%
h 124
10.6%
c 119
10.2%
n 114
9.8%
d 98
8.4%
e 96
8.2%
s 90
 
7.7%
t 69
 
5.9%
b 61
 
5.2%
k 58
 
5.0%
Other values (8) 102
8.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 235
20.2%
h 124
10.6%
c 119
10.2%
n 114
9.8%
d 98
8.4%
e 96
8.2%
s 90
 
7.7%
t 69
 
5.9%
b 61
 
5.2%
k 58
 
5.0%
Other values (8) 102
8.7%

drive-wheels
Categorical

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
fwd
105 
rwd
67 
4wd
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 105
58.3%
rwd 67
37.2%
4wd 8
 
4.4%

Length

2024-05-28T15:00:28.815054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:29.048781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd 105
58.3%
rwd 67
37.2%
4wd 8
 
4.4%

Most occurring characters

ValueCountFrequency (%)
w 180
33.3%
d 180
33.3%
f 105
19.4%
r 67
 
12.4%
4 8
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 180
33.3%
d 180
33.3%
f 105
19.4%
r 67
 
12.4%
4 8
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 180
33.3%
d 180
33.3%
f 105
19.4%
r 67
 
12.4%
4 8
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 180
33.3%
d 180
33.3%
f 105
19.4%
r 67
 
12.4%
4 8
 
1.5%

engine-location
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
front
177 
rear
 
3

Length

Max length5
Median length5
Mean length4.9833333
Min length4

Characters and Unicode

Total characters897
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 177
98.3%
rear 3
 
1.7%

Length

2024-05-28T15:00:29.359216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:29.628520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
front 177
98.3%
rear 3
 
1.7%

Most occurring characters

ValueCountFrequency (%)
r 183
20.4%
f 177
19.7%
o 177
19.7%
n 177
19.7%
t 177
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 183
20.4%
f 177
19.7%
o 177
19.7%
n 177
19.7%
t 177
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 183
20.4%
f 177
19.7%
o 177
19.7%
n 177
19.7%
t 177
19.7%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 897
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 183
20.4%
f 177
19.7%
o 177
19.7%
n 177
19.7%
t 177
19.7%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

Distinct46
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.89
Minimum88.4
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:29.877275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum88.4
5-th percentile93.095
Q194.5
median97
Q3102.1
95-th percentile112.05
Maximum120.9
Range32.5
Interquartile range (IQR)7.6

Descriptive statistics

Standard deviation6.0919408
Coefficient of variation (CV)0.061603204
Kurtosis1.0004359
Mean98.89
Median Absolute Deviation (MAD)2.5
Skewness1.1468247
Sum17800.2
Variance37.111743
MonotonicityNot monotonic
2024-05-28T15:00:30.214037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
94.5 19
 
10.6%
93.7 18
 
10.0%
95.7 13
 
7.2%
97.3 7
 
3.9%
96.5 7
 
3.9%
99.1 6
 
3.3%
107.9 6
 
3.3%
98.4 6
 
3.3%
98.8 6
 
3.3%
96.3 6
 
3.3%
Other values (36) 86
47.8%
ValueCountFrequency (%)
88.4 1
 
0.6%
88.6 2
 
1.1%
89.5 3
 
1.7%
91.3 2
 
1.1%
93 1
 
0.6%
93.1 5
 
2.8%
93.7 18
10.0%
94.5 19
10.6%
95.1 1
 
0.6%
95.3 4
 
2.2%
ValueCountFrequency (%)
120.9 1
 
0.6%
115.6 2
 
1.1%
114.2 4
2.2%
113 2
 
1.1%
112 1
 
0.6%
110 2
 
1.1%
109.1 5
2.8%
108 1
 
0.6%
107.9 6
3.3%
106.7 1
 
0.6%

length
Real number (ℝ)

Distinct63
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.25611
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:30.810555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.675
median172.5
Q3184.6
95-th percentile198.9
Maximum208.1
Range67
Interquartile range (IQR)17.925

Descriptive statistics

Standard deviation12.255274
Coefficient of variation (CV)0.070329094
Kurtosis-0.14572653
Mean174.25611
Median Absolute Deviation (MAD)6.2
Skewness0.27899306
Sum31366.1
Variance150.19175
MonotonicityNot monotonic
2024-05-28T15:00:31.505269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 14
 
7.8%
188.8 11
 
6.1%
171.7 7
 
3.9%
166.3 7
 
3.9%
186.7 7
 
3.9%
165.3 6
 
3.3%
177.8 6
 
3.3%
186.6 6
 
3.3%
172 5
 
2.8%
175.6 5
 
2.8%
Other values (53) 106
58.9%
ValueCountFrequency (%)
141.1 1
 
0.6%
150 3
 
1.7%
155.9 3
 
1.7%
157.1 1
 
0.6%
157.3 14
7.8%
158.7 3
 
1.7%
158.8 1
 
0.6%
159.1 3
 
1.7%
162.4 1
 
0.6%
163.4 1
 
0.6%
ValueCountFrequency (%)
208.1 1
 
0.6%
202.6 2
1.1%
199.6 2
1.1%
199.2 1
 
0.6%
198.9 4
2.2%
193.8 1
 
0.6%
192.7 3
1.7%
191.7 1
 
0.6%
190.9 2
1.1%
189 2
1.1%

width
Real number (ℝ)

Distinct38
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.926667
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:32.104826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.675
95-th percentile70.315
Maximum72.3
Range12
Interquartile range (IQR)2.575

Descriptive statistics

Standard deviation2.1539259
Coefficient of variation (CV)0.032671542
Kurtosis0.72566764
Mean65.926667
Median Absolute Deviation (MAD)1.4
Skewness0.93798775
Sum11866.8
Variance4.6393966
MonotonicityNot monotonic
2024-05-28T15:00:32.400107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
63.8 23
 
12.8%
66.5 21
 
11.7%
65.4 15
 
8.3%
63.6 10
 
5.6%
64.4 10
 
5.6%
68.4 10
 
5.6%
64 8
 
4.4%
65.5 8
 
4.4%
65.2 6
 
3.3%
67.2 6
 
3.3%
Other values (28) 63
35.0%
ValueCountFrequency (%)
60.3 1
 
0.6%
62.5 1
 
0.6%
63.6 10
5.6%
63.8 23
12.8%
63.9 1
 
0.6%
64 8
 
4.4%
64.1 2
 
1.1%
64.2 5
 
2.8%
64.4 10
5.6%
64.8 4
 
2.2%
ValueCountFrequency (%)
72.3 1
 
0.6%
72 1
 
0.6%
71.7 3
 
1.7%
71.4 3
 
1.7%
70.6 1
 
0.6%
70.3 3
 
1.7%
69.6 2
 
1.1%
68.9 4
 
2.2%
68.8 1
 
0.6%
68.4 10
5.6%

height
Real number (ℝ)

Distinct47
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.757222
Minimum47.8
Maximum59.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:32.699375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.695
Q152
median54.1
Q355.55
95-th percentile57.5
Maximum59.1
Range11.3
Interquartile range (IQR)3.55

Descriptive statistics

Standard deviation2.4064631
Coefficient of variation (CV)0.044765392
Kurtosis-0.52111377
Mean53.757222
Median Absolute Deviation (MAD)1.6
Skewness-0.052230113
Sum9676.3
Variance5.7910649
MonotonicityNot monotonic
2024-05-28T15:00:32.952612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
55.7 11
 
6.1%
50.8 11
 
6.1%
52 10
 
5.6%
54.5 10
 
5.6%
55.5 9
 
5.0%
54.3 8
 
4.4%
56.7 8
 
4.4%
54.1 7
 
3.9%
52.6 7
 
3.9%
56.1 7
 
3.9%
Other values (37) 92
51.1%
ValueCountFrequency (%)
47.8 1
 
0.6%
48.8 2
 
1.1%
49.4 2
 
1.1%
49.6 4
 
2.2%
49.7 3
 
1.7%
50.2 2
 
1.1%
50.5 2
 
1.1%
50.6 5
2.8%
50.8 11
6.1%
51 1
 
0.6%
ValueCountFrequency (%)
59.1 3
 
1.7%
58.7 4
2.2%
58.3 1
 
0.6%
57.5 3
 
1.7%
56.7 8
4.4%
56.5 2
 
1.1%
56.3 1
 
0.6%
56.2 3
 
1.7%
56.1 7
3.9%
56 1
 
0.6%

curb-weight
Real number (ℝ)

Distinct149
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2543.8
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:33.271591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1904.75
Q12137
median2404
Q32917.75
95-th percentile3496
Maximum4066
Range2578
Interquartile range (IQR)780.75

Descriptive statistics

Standard deviation525.22964
Coefficient of variation (CV)0.20647442
Kurtosis0.04742909
Mean2543.8
Median Absolute Deviation (MAD)378
Skewness0.75685268
Sum457884
Variance275866.17
MonotonicityNot monotonic
2024-05-28T15:00:34.407358image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.2%
2275 3
 
1.7%
1989 3
 
1.7%
1918 3
 
1.7%
2410 2
 
1.1%
2191 2
 
1.1%
2024 2
 
1.1%
2414 2
 
1.1%
4066 2
 
1.1%
2300 2
 
1.1%
Other values (139) 155
86.1%
ValueCountFrequency (%)
1488 1
 
0.6%
1837 1
 
0.6%
1874 2
1.1%
1876 2
1.1%
1889 1
 
0.6%
1890 1
 
0.6%
1900 1
 
0.6%
1905 1
 
0.6%
1909 2
1.1%
1918 3
1.7%
ValueCountFrequency (%)
4066 2
1.1%
3950 1
0.6%
3900 1
0.6%
3770 1
0.6%
3750 1
0.6%
3740 1
0.6%
3715 1
0.6%
3515 1
0.6%
3495 1
0.6%
3485 1
0.6%

engine-type
Categorical

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
ohc
131 
ohcv
 
12
l
 
12
ohcf
 
12
dohc
 
8
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1
Min length1

Characters and Unicode

Total characters558
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 131
72.8%
ohcv 12
 
6.7%
l 12
 
6.7%
ohcf 12
 
6.7%
dohc 8
 
4.4%
rotor 4
 
2.2%
dohcv 1
 
0.6%

Length

2024-05-28T15:00:34.747574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:35.045161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc 131
72.8%
ohcv 12
 
6.7%
l 12
 
6.7%
ohcf 12
 
6.7%
dohc 8
 
4.4%
rotor 4
 
2.2%
dohcv 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 172
30.8%
h 164
29.4%
c 164
29.4%
v 13
 
2.3%
l 12
 
2.2%
f 12
 
2.2%
d 9
 
1.6%
r 8
 
1.4%
t 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 172
30.8%
h 164
29.4%
c 164
29.4%
v 13
 
2.3%
l 12
 
2.2%
f 12
 
2.2%
d 9
 
1.6%
r 8
 
1.4%
t 4
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 172
30.8%
h 164
29.4%
c 164
29.4%
v 13
 
2.3%
l 12
 
2.2%
f 12
 
2.2%
d 9
 
1.6%
r 8
 
1.4%
t 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 558
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 172
30.8%
h 164
29.4%
c 164
29.4%
v 13
 
2.3%
l 12
 
2.2%
f 12
 
2.2%
d 9
 
1.6%
r 8
 
1.4%
t 4
 
0.7%

num-of-cylinders
Categorical

IMBALANCE 

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
four
141 
six
19 
five
 
10
two
 
4
eight
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9111111
Min length3

Characters and Unicode

Total characters704
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 141
78.3%
six 19
 
10.6%
five 10
 
5.6%
two 4
 
2.2%
eight 4
 
2.2%
three 1
 
0.6%
twelve 1
 
0.6%

Length

2024-05-28T15:00:35.290661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:35.515113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 141
78.3%
six 19
 
10.6%
five 10
 
5.6%
two 4
 
2.2%
eight 4
 
2.2%
three 1
 
0.6%
twelve 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
f 151
21.4%
o 145
20.6%
r 142
20.2%
u 141
20.0%
i 33
 
4.7%
s 19
 
2.7%
x 19
 
2.7%
e 18
 
2.6%
v 11
 
1.6%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 151
21.4%
o 145
20.6%
r 142
20.2%
u 141
20.0%
i 33
 
4.7%
s 19
 
2.7%
x 19
 
2.7%
e 18
 
2.6%
v 11
 
1.6%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 151
21.4%
o 145
20.6%
r 142
20.2%
u 141
20.0%
i 33
 
4.7%
s 19
 
2.7%
x 19
 
2.7%
e 18
 
2.6%
v 11
 
1.6%
t 10
 
1.4%
Other values (4) 15
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 151
21.4%
o 145
20.6%
r 142
20.2%
u 141
20.0%
i 33
 
4.7%
s 19
 
2.7%
x 19
 
2.7%
e 18
 
2.6%
v 11
 
1.6%
t 10
 
1.4%
Other values (4) 15
 
2.1%

engine-size
Real number (ℝ)

Distinct41
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.59444
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:35.770728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median114.5
Q3141
95-th percentile194.45
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation42.1439
Coefficient of variation (CV)0.33555545
Kurtosis5.9557612
Mean125.59444
Median Absolute Deviation (MAD)19.5
Skewness2.0874848
Sum22607
Variance1776.1083
MonotonicityNot monotonic
2024-05-28T15:00:36.010247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
98 14
 
7.8%
122 13
 
7.2%
92 13
 
7.2%
90 12
 
6.7%
97 12
 
6.7%
108 11
 
6.1%
110 11
 
6.1%
141 7
 
3.9%
120 7
 
3.9%
121 6
 
3.3%
Other values (31) 74
41.1%
ValueCountFrequency (%)
61 1
 
0.6%
70 3
 
1.7%
79 1
 
0.6%
80 1
 
0.6%
90 12
6.7%
91 5
 
2.8%
92 13
7.2%
97 12
6.7%
98 14
7.8%
103 1
 
0.6%
ValueCountFrequency (%)
326 1
 
0.6%
308 1
 
0.6%
304 1
 
0.6%
258 2
 
1.1%
234 1
 
0.6%
209 2
 
1.1%
203 1
 
0.6%
194 3
1.7%
183 4
2.2%
181 6
3.3%

fuel-system
Categorical

Distinct7
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
mpfi
82 
2bbl
60 
idi
19 
1bbl
spdi
 
6
Other values (2)
 
4

Length

Max length4
Median length4
Mean length3.8944444
Min length3

Characters and Unicode

Total characters701
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 82
45.6%
2bbl 60
33.3%
idi 19
 
10.6%
1bbl 9
 
5.0%
spdi 6
 
3.3%
4bbl 3
 
1.7%
spfi 1
 
0.6%

Length

2024-05-28T15:00:36.831064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T15:00:37.033318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 82
45.6%
2bbl 60
33.3%
idi 19
 
10.6%
1bbl 9
 
5.0%
spdi 6
 
3.3%
4bbl 3
 
1.7%
spfi 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
b 144
20.5%
i 127
18.1%
p 89
12.7%
f 83
11.8%
m 82
11.7%
l 72
10.3%
2 60
8.6%
d 25
 
3.6%
1 9
 
1.3%
s 7
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 144
20.5%
i 127
18.1%
p 89
12.7%
f 83
11.8%
m 82
11.7%
l 72
10.3%
2 60
8.6%
d 25
 
3.6%
1 9
 
1.3%
s 7
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 144
20.5%
i 127
18.1%
p 89
12.7%
f 83
11.8%
m 82
11.7%
l 72
10.3%
2 60
8.6%
d 25
 
3.6%
1 9
 
1.3%
s 7
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 144
20.5%
i 127
18.1%
p 89
12.7%
f 83
11.8%
m 82
11.7%
l 72
10.3%
2 60
8.6%
d 25
 
3.6%
1 9
 
1.3%
s 7
 
1.0%

bore
Real number (ℝ)

Distinct35
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3265
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:37.508279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.13
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.27634582
Coefficient of variation (CV)0.083074047
Kurtosis-0.79192217
Mean3.3265
Median Absolute Deviation (MAD)0.26
Skewness0.057693298
Sum598.77
Variance0.076367011
MonotonicityNot monotonic
2024-05-28T15:00:37.804804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3.62 18
 
10.0%
3.19 16
 
8.9%
3.15 14
 
7.8%
3.03 12
 
6.7%
2.97 12
 
6.7%
3.31 11
 
6.1%
3.78 8
 
4.4%
3.46 8
 
4.4%
3.43 8
 
4.4%
3.39 6
 
3.3%
Other values (25) 67
37.2%
ValueCountFrequency (%)
2.54 1
 
0.6%
2.68 1
 
0.6%
2.91 5
2.8%
2.92 1
 
0.6%
2.97 12
6.7%
2.99 1
 
0.6%
3.01 4
 
2.2%
3.03 12
6.7%
3.05 6
3.3%
3.08 1
 
0.6%
ValueCountFrequency (%)
3.94 2
 
1.1%
3.8 2
 
1.1%
3.78 8
4.4%
3.76 1
 
0.6%
3.74 3
 
1.7%
3.7 5
 
2.8%
3.63 2
 
1.1%
3.62 18
10.0%
3.61 1
 
0.6%
3.58 6
 
3.3%

stroke
Real number (ℝ)

Distinct35
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2413333
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:38.068369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.27
Q33.4
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.3034244
Coefficient of variation (CV)0.093610984
Kurtosis2.5782825
Mean3.2413333
Median Absolute Deviation (MAD)0.145
Skewness-0.69511723
Sum583.44
Variance0.092066369
MonotonicityNot monotonic
2024-05-28T15:00:38.282465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3.4 15
 
8.3%
3.15 14
 
7.8%
3.03 14
 
7.8%
3.23 13
 
7.2%
3.29 13
 
7.2%
3.39 12
 
6.7%
2.64 9
 
5.0%
3.27 6
 
3.3%
3.46 6
 
3.3%
3.19 6
 
3.3%
Other values (25) 72
40.0%
ValueCountFrequency (%)
2.07 1
 
0.6%
2.19 2
 
1.1%
2.64 9
5.0%
2.68 2
 
1.1%
2.76 1
 
0.6%
2.8 2
 
1.1%
2.87 1
 
0.6%
2.9 3
 
1.7%
3.03 14
7.8%
3.07 6
3.3%
ValueCountFrequency (%)
4.17 2
 
1.1%
3.9 2
 
1.1%
3.86 1
 
0.6%
3.64 5
2.8%
3.58 5
2.8%
3.54 4
2.2%
3.52 5
2.8%
3.5 5
2.8%
3.47 4
2.2%
3.46 6
3.3%

compression-ratio
Real number (ℝ)

Distinct32
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.285667
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:38.526789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.6
Q18.7
median9
Q39.4
95-th percentile21.905
Maximum23
Range16
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation4.0751908
Coefficient of variation (CV)0.39620094
Kurtosis4.5504142
Mean10.285667
Median Absolute Deviation (MAD)0.4
Skewness2.4901575
Sum1851.42
Variance16.60718
MonotonicityNot monotonic
2024-05-28T15:00:38.911228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 43
23.9%
9.4 26
14.4%
9.5 13
 
7.2%
8.5 8
 
4.4%
9.3 8
 
4.4%
8 7
 
3.9%
8.7 7
 
3.9%
9.2 5
 
2.8%
21 5
 
2.8%
8.4 5
 
2.8%
Other values (22) 53
29.4%
ValueCountFrequency (%)
7 3
 
1.7%
7.5 5
2.8%
7.6 4
2.2%
7.7 2
 
1.1%
7.8 1
 
0.6%
8 7
3.9%
8.1 2
 
1.1%
8.3 2
 
1.1%
8.4 5
2.8%
8.5 8
4.4%
ValueCountFrequency (%)
23 4
2.2%
22.7 1
 
0.6%
22.5 3
1.7%
22 1
 
0.6%
21.9 1
 
0.6%
21.5 4
2.2%
21 5
2.8%
11.5 1
 
0.6%
10.1 1
 
0.6%
10 3
1.7%

horsepower
Real number (ℝ)

Distinct55
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.98333
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:39.333426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile182
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.684999
Coefficient of variation (CV)0.38535361
Kurtosis3.3540729
Mean102.98333
Median Absolute Deviation (MAD)25
Skewness1.5635713
Sum18537
Variance1574.8992
MonotonicityNot monotonic
2024-05-28T15:00:39.593303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 18
 
10.0%
70 11
 
6.1%
69 9
 
5.0%
95 9
 
5.0%
116 8
 
4.4%
110 7
 
3.9%
62 6
 
3.3%
114 6
 
3.3%
160 6
 
3.3%
102 5
 
2.8%
Other values (45) 95
52.8%
ValueCountFrequency (%)
48 1
 
0.6%
52 2
 
1.1%
55 1
 
0.6%
56 2
 
1.1%
60 1
 
0.6%
62 6
 
3.3%
64 1
 
0.6%
68 18
10.0%
69 9
5.0%
70 11
6.1%
ValueCountFrequency (%)
288 1
 
0.6%
262 1
 
0.6%
207 3
1.7%
200 1
 
0.6%
184 2
 
1.1%
182 2
 
1.1%
176 2
 
1.1%
175 1
 
0.6%
162 2
 
1.1%
160 6
3.3%

peak-rpm
Real number (ℝ)

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5130.5556
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:39.988686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4200
Q14800
median5200
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation487.22188
Coefficient of variation (CV)0.094964742
Kurtosis0.069606041
Mean5130.5556
Median Absolute Deviation (MAD)300
Skewness0.045499992
Sum923500
Variance237385.16
MonotonicityNot monotonic
2024-05-28T15:00:40.246930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5500 33
18.3%
4800 33
18.3%
5000 21
11.7%
5200 21
11.7%
5400 12
 
6.7%
6000 8
 
4.4%
5250 7
 
3.9%
5800 6
 
3.3%
4500 6
 
3.3%
4150 5
 
2.8%
Other values (12) 28
15.6%
ValueCountFrequency (%)
4150 5
 
2.8%
4200 5
 
2.8%
4250 3
 
1.7%
4350 4
 
2.2%
4400 1
 
0.6%
4500 6
 
3.3%
4650 1
 
0.6%
4750 3
 
1.7%
4800 33
18.3%
5000 21
11.7%
ValueCountFrequency (%)
6600 2
 
1.1%
6000 8
 
4.4%
5900 3
 
1.7%
5800 6
 
3.3%
5750 1
 
0.6%
5600 1
 
0.6%
5500 33
18.3%
5400 12
 
6.7%
5300 1
 
0.6%
5250 7
 
3.9%

city-mpg
Real number (ℝ)

Distinct27
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.427778
Minimum13
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:40.468672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median25
Q330.25
95-th percentile37.05
Maximum47
Range34
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation6.47473
Coefficient of variation (CV)0.25463216
Kurtosis0.033481356
Mean25.427778
Median Absolute Deviation (MAD)6
Skewness0.46572599
Sum4577
Variance41.922129
MonotonicityNot monotonic
2024-05-28T15:00:40.711905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
31 26
14.4%
19 20
11.1%
24 15
 
8.3%
27 14
 
7.8%
17 13
 
7.2%
23 12
 
6.7%
26 10
 
5.6%
30 8
 
4.4%
21 8
 
4.4%
38 7
 
3.9%
Other values (17) 47
26.1%
ValueCountFrequency (%)
13 1
 
0.6%
14 2
 
1.1%
15 2
 
1.1%
16 5
 
2.8%
17 13
7.2%
18 3
 
1.7%
19 20
11.1%
20 1
 
0.6%
21 8
 
4.4%
22 4
 
2.2%
ValueCountFrequency (%)
47 1
 
0.6%
45 1
 
0.6%
38 7
 
3.9%
37 6
 
3.3%
36 1
 
0.6%
35 1
 
0.6%
34 1
 
0.6%
32 1
 
0.6%
31 26
14.4%
30 8
 
4.4%

highway-mpg
Real number (ℝ)

Distinct29
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.033333
Minimum16
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:40.936393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median31
Q337
95-th percentile43
Maximum53
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.8011665
Coefficient of variation (CV)0.21915682
Kurtosis0.10118164
Mean31.033333
Median Absolute Deviation (MAD)6
Skewness0.38185149
Sum5586
Variance46.255866
MonotonicityNot monotonic
2024-05-28T15:00:41.331338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
25 19
 
10.6%
32 16
 
8.9%
38 15
 
8.3%
34 14
 
7.8%
30 13
 
7.2%
37 13
 
7.2%
28 12
 
6.7%
33 9
 
5.0%
22 8
 
4.4%
24 8
 
4.4%
Other values (19) 53
29.4%
ValueCountFrequency (%)
16 2
 
1.1%
17 1
 
0.6%
18 1
 
0.6%
19 2
 
1.1%
20 1
 
0.6%
22 8
4.4%
23 7
 
3.9%
24 8
4.4%
25 19
10.6%
26 3
 
1.7%
ValueCountFrequency (%)
53 1
 
0.6%
50 1
 
0.6%
47 2
 
1.1%
46 2
 
1.1%
43 4
 
2.2%
42 3
 
1.7%
41 3
 
1.7%
39 2
 
1.1%
38 15
8.3%
37 13
7.2%

price
Real number (ℝ)

Distinct162
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13085.783
Minimum5151
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2024-05-28T15:00:41.611501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5151
5-th percentile6184.3
Q17784.75
median10221.5
Q316506
95-th percentile32263.9
Maximum45400
Range40249
Interquartile range (IQR)8721.25

Descriptive statistics

Standard deviation7929.788
Coefficient of variation (CV)0.60598497
Kurtosis3.3510351
Mean13085.783
Median Absolute Deviation (MAD)3213
Skewness1.8345473
Sum2355441
Variance62881537
MonotonicityNot monotonic
2024-05-28T15:00:41.847038image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10295 5
 
2.8%
7609 2
 
1.1%
18150 2
 
1.1%
7898 2
 
1.1%
7775 2
 
1.1%
8845 2
 
1.1%
7295 2
 
1.1%
6692 2
 
1.1%
6229 2
 
1.1%
7957 2
 
1.1%
Other values (152) 157
87.2%
ValueCountFrequency (%)
5151 1
0.6%
5195 1
0.6%
5348 1
0.6%
5389 1
0.6%
5399 1
0.6%
5499 1
0.6%
5572 2
1.1%
6095 1
0.6%
6189 1
0.6%
6229 2
1.1%
ValueCountFrequency (%)
45400 1
0.6%
41315 1
0.6%
40960 1
0.6%
37028 1
0.6%
36000 1
0.6%
35550 1
0.6%
34184 1
0.6%
34028 1
0.6%
32528 1
0.6%
32250 1
0.6%

Interactions

2024-05-28T15:00:21.094409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.021486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:39.494467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:41.907380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:44.473951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.028424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:49.741698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.049100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:55.791129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:58.488836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:02.289784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:05.310843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:08.652266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:11.784290image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:14.553870image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:17.980821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:21.248208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.183996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:39.643198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.067878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:44.591695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.162865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:49.924390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.228569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:55.942754image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:58.649704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:02.458422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:05.504960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:08.914991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.060085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:14.715406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:18.173342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:21.445470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.584203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:39.832176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.189887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:44.729460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.321364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:50.091635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.394909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.101916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:58.843504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:02.621116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:05.719058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:09.217628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.239645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:14.879678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:18.349713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:21.614389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.723341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.099872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.328849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:44.970777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.483591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:50.251208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.549307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.263783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:59.035249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:02.803539image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:05.915370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:09.394658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.406909image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:15.069961image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:18.528447image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:21.795287image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.857566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.230189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.471611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:45.137516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.635548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:50.417185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.752060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.443852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:59.207586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:02.956351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:06.135157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:09.574625image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.578710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:15.228103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:18.712675image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:22.023753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:37.976858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.394811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.593515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:45.284322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.780111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:51.044001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:53.949975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.591746image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:59.391920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:03.120256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:06.338346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:09.750438image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.731885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:15.399384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:18.890694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:22.194973image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:38.094850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.523597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:42.712432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:45.407723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:47.929333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:51.334057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:54.121352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.760037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:59.636389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:03.272698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:06.538265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:09.936834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:12.886928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:15.570862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:19.087191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:22.349609image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:38.206159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.654995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:43.185735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:45.531739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:48.135527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:51.484163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:54.281575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:56.928778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:59.846098image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:03.438324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:06.790260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:10.106353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:13.017146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:15.750679image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:19.256316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:22.530211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:38.325578image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.786131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:43.318374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:45.668298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:48.350605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:51.656681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:54.440408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:57.084409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:00.016159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:03.717548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:06.993972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:10.275148image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:13.162989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:16.492405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:19.426201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:22.740383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:38.453826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:40.924284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:43.459490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:59:48.565761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:59:51.842701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:59:57.260742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T15:00:10.777409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T15:00:13.807918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T15:00:11.592014image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:14.314431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:17.741514image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T15:00:20.889458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-28T15:00:24.208240image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-28T15:00:24.969295image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03115.0alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.0
13115.0alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.0
21115.0alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.0
32164.0audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.0
42164.0audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.0
52115.0audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.0
61158.0audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.0
71115.0audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.0
81158.0audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.0
90115.0audigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.0160.05500.0162210295.0
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
195-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.0
196-2103.0volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.0
197-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.0
198-2103.0volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.0
199-174.0volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.0
200-195.0volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.0
201-195.0volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.0
202-195.0volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.0
203-195.0volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.0262722470.0
204-195.0volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.0